Publication in EndNote Format

%0 Report
%A Vinay K. Chaudhri and Peter E. Clark and Sunil Mishra and John Pacheco and Aaron
Spaulding and Jing Tien
%T AURA: Capturing Knowledge and Answering Questions on Science Textbooks
%I Proceedings of the 4th international conference on Knowledge capture
%D 2007
%X AURA is an AI-motivated system with a healthy intersec-tion with the sciences:
its short-term goal is to enable do-main experts to construct declarative knowledge
bases (KBs) from 50 pages of a science textbook for Physics, Chemistry, and Biology
in a way that another user can pose questions similar to those in an Advanced
Placement (AP) exam and get answers and explanations. In building AURA, and in
line with the conference theme of The Inter-disciplinary Reach of AI, a key question
and challenge has been: How much of the knowledge in the three domains can be
captured through a generic knowledge capture and rea-soning capability and to
what extent does it need to be spe-cialized for each domain? This paper contributes
an answer to this question based on the experience of designing and implementing
AURA. It also presents the first concise dis-tillation of the key ideas in AURA,
integrating ideas from previous specialized publications.
%U http://www.ai.sri.com/pubs/files/1768.pdf